starting out

.gitignore

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+Datasets/

test.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting started"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch\n",
+    "import torchvision.transforms as transforms\n",
+    "import torchvision.models as models\n",
+    "from torchinfo import summary\n",
+    "\n",
+    "\n",
+    "from PIL import Image\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "%matplotlib inline\n",
+    "\n",
+    "dev = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "print(f'Using {dev} for inference')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Caltech101 dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torchvision.datasets as datasets\n",
+    "\n",
+    "dataset_path = \"C:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Datasets\"\n",
+    "\n",
+    "dataset = datasets.Caltech101(root=\"C:\\Kaushik\\ASU\\CSE 515 - Multimedia and Web Databases\\Project\\Datasets\", download=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualize a sample image from the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "sample_image, _ = dataset.__getitem__(random.randint(0,len(dataset)))\n",
+    "plt.imshow(sample_image)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ResNet50 - Example classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1: Load model\n",
+    "from torchvision.models import ResNet50_Weights\n",
+    "\n",
+    "weights = ResNet50_Weights.DEFAULT\n",
+    "model = models.resnet50(weights)\n",
+    "\n",
+    "if(torch.cuda.is_available()):\n",
+    "    model = model.to(dev)\n",
+    "\n",
+    "model.eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchvision.io import read_image\n",
+    "\n",
+    "# Step 2: Initialize the inference transforms\n",
+    "preprocess = weights.transforms()\n",
+    "\n",
+    "img = sample_image\n",
+    "# Step 3: Apply inference preprocessing transforms\n",
+    "batch = preprocess(img).unsqueeze(0)\n",
+    "\n",
+    "# (convert to CUDA tensor)\n",
+    "batch = batch.to(dev)\n",
+    "# Step 4: Use the model and print the predicted category\n",
+    "\n",
+    "prediction = model(batch).squeeze(0).softmax(0)\n",
+    "class_id = prediction.argmax().item()\n",
+    "score = prediction[class_id].item()\n",
+    "category_name = weights.meta[\"categories\"][class_id]\n",
+    "print(f\"{category_name}: {100 * score:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}